Now calonder descriptor loads features from storage (if they are exists there)

git-svn-id: https://code.ros.org/svn/opencv/trunk@2453 73c94f0f-984f-4a5f-82bc-2d8db8d8ee08

diff --git a/opencv/tests/cv/src/acalonder.cpp b/opencv/tests/cv/src/acalonder.cpp
index 49accb96daff22c9894daf09e44d84a255e5eb46..4f6da77e6b7205acf2e6f7f95ab536019400c6f7 100644 (file)
--- a/opencv/tests/cv/src/acalonder.cpp
+++ b/opencv/tests/cv/src/acalonder.cpp
@@ -71,11 +71,14 @@ protected:
        void RotateAndScale(const IplImage* src, IplImage* dst, float angle, float scale_x, float scale_y);
        // Scales the source image point and rotate to the angle (Positive values mean counter-clockwise rotation) 
        void RotateAndScale(const CvPoint& src, CvPoint& dst, const CvPoint& center, float angle, float scale_x, float scale_y);
-       float RunTestsSeries(const IplImage* train_image/*, bool drawResults = false*/);
+       float RunTestsSeries(const IplImage* train_image, vector<CvPoint>& keypoints/*, bool drawResults = false*/);
        //returns 1 in the case of success, 0 otherwise
        int SaveKeypoints(const vector<CvPoint>& points, const char* path);
        ////returns 1 in the case of success, 0 otherwise
-       //int LoadKeypoints(vector<CvPoint>& points, const char* path);
+       int LoadKeypoints(vector<CvPoint>& points, const char* path);
+
+       void GetKeypointSignature(IplImage* test_image, int patch_size, vector<float>& signature);
+
 
 };
 
@@ -329,7 +332,7 @@ void CV_CalonderTest::RotateAndScale(const CvPoint& src, CvPoint& dst, const CvP
        cvReleaseMat(&transform);
 }
 
-float CV_CalonderTest::RunTestsSeries(const IplImage* train_image)
+float CV_CalonderTest::RunTestsSeries(const IplImage* train_image, vector<CvPoint>& keypoints)
 {
        float angles[] = {-CV_PI/4,CV_PI/4};
        float scales_x[] = {0.85,1.15};
@@ -354,7 +357,22 @@ float CV_CalonderTest::RunTestsSeries(const IplImage* train_image)
        cvResetImageROI(test_image);
 
        vector<CvPoint> objectKeypoints;
-       ExtractFeatures(train_image,objectKeypoints);
+       if (keypoints.size() < 1)
+       {
+               ExtractFeatures(train_image,objectKeypoints);
+               for (int i=0;i<objectKeypoints.size();i++)
+               {
+                       keypoints.push_back(objectKeypoints[i]);
+               }
+       }
+       else
+       {
+               for (int i=0;i<keypoints.size();i++)
+               {
+                       objectKeypoints.push_back(keypoints[i]);
+               }
+       }
+
        RTreeClassifier detector;
        int patch_size = PATCH_SIZE;
        //this->update_progress(1,0,total_cases,5);
@@ -452,13 +470,34 @@ int CV_CalonderTest::SaveKeypoints(const vector<CvPoint>& points, const char* pa
                fprintf(f,"%d,%d\n",points[i].x,points[i].y);
        }
        fclose(f);
-       return -1;
+       return 1;
+}
+
+int CV_CalonderTest::LoadKeypoints(vector<CvPoint>& points, const char* path)
+{
+       FILE* f = fopen(path,"r");
+       points.clear();
+
+       if (f==NULL)
+       {
+               return 0;
+       }
+       
+       while (!feof(f))
+       {
+               int x,y;
+               fscanf(f,"%d,%d\n",&x,&y);
+               points.push_back(cvPoint(x,y));
+       }
+       fclose(f);
+       return 1;
 }
 
 
 void CV_CalonderTest::run( int /* start_from */)
 {
        string train_image_path = string(ts->get_data_path()) + "calonder/baboon200.jpg";
+       string train_keypoints_path = string(ts->get_data_path()) + "calonder/train_features.txt";
        IplImage* train_image = cvLoadImage(train_image_path.c_str(),0);
 
        if (!train_image)
@@ -467,9 +506,23 @@ void CV_CalonderTest::run( int /* start_from */)
                ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
                return;
        }
+
+
+
        // Testing rtree classifier
        float min_accuracy = 0.35;
-       float correctness = RunTestsSeries(train_image);
+       vector<CvPoint> train_keypoints;
+       train_keypoints.clear();
+       float correctness;
+       if (!LoadKeypoints(train_keypoints,train_keypoints_path.c_str()))
+       {
+               correctness = RunTestsSeries(train_image,train_keypoints);
+               SaveKeypoints(train_keypoints,train_keypoints_path.c_str());
+       }
+       else
+       {
+               correctness = RunTestsSeries(train_image,train_keypoints);
+       }
        if (correctness > min_accuracy)
                ts->set_failed_test_info(CvTS::OK);
        else